Doubly robust estimation, optimally truncated inverse-intensity weighting and increment-based methods for the analysis of irregularly observed longitudinal data

Authors

Errata

This article is corrected by:

  1. Errata: Correction for “Doubly robust estimation, optimally truncated inverse-intensity weighting and increment-based methods for the analysis of irregularly observed longitudinal data” by Eleanor Pullenayegum and Brian Feldman Volume 33, Issue 3, 540, Article first published online: 23 August 2013

Correspondence to: Eleanor M. Pullenayegum, Biostatistics Unit, St Joseph's Healthcare, 50 Charlton Ave E.

E-mail: pullena@mcmaster.ca

Abstract

Longitudinal data arising from routine follow-up of patients will often have irregular measurement times. Existing methods for analysis include joint modelling of the outcome and measurement processes, and inverse-intensity weighting (IIW). This work extends previously proposed analysis of increments to the case of irregular follow-up, yielding a model for the increments that can be used as a stand-alone method. Furthermore, we propose two ways of combining the increments and IIW estimators. First, we use the increment model to select the truncation point for the inverse-intensity weights that minimises the mean squared error of the IIW estimator. Second, we use the increment model to augment the usual IIW estimating equations to form a doubly robust estimator. We evaluate the methods through simulation and apply these to a recent study of juvenile dermatomyositis. Copyright © 2012 John Wiley & Sons, Ltd.

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